Application of spectral features for separating homochromatic foreign matter from mixed congee

• Published in: Food Chemistry: X 2021-10, Vol.11, p.100128-100128, Article 100128
• Main Authors: Shi, Jiyong, Wang, Yueying, Liu, Chuanpeng, Li, Zhihua, Huang, Xiaowei, Guo, Zhiming, Zhang, Xinai, Zhang, Di, Zou, Xiaobo
• Format: Article
• Language: English
• Subjects: Chemometrics; Homochromatic foreign matter; Hyperspectral imaging technology; Mixed congee; Pattern recognition
• ISSN: 2590-1575; 2590-1575
• DOI: 10.1016/j.fochx.2021.100128

•A method that can separate homochromatic FM in mixed congee was proposed.•Spectral features of FM and mixed congee were extracted to build recognition model.•The SVM model achieved high identification rates (99.17%) for homochromatic FM.•The proposed method is better than computer vision in separating homochromatic FM. Foreign matter (FM) in mixed congee not only reduces the quality of the congee but may also harm consumers. However, the common computer vision methods with poor recognition ability for the homochromatic FM. This study used hyperspectral reflectance images with the pattern recognition model to detect homochromatic FM on the mixed congee surface. First, spectral features corresponding to homochromatic FM and background were extracted from hyperspectral images. Then, based on the optimal spectral preprocessing method, LDA, K-nearest neighbor, backpropagation artificial neural network, and support vector machine (SVM) were used to classify the spectral features. The results revealed that the SVM model input with raw spectra principal components exhibited optimal identification rates of 99.17%. Finally, most of the pixels for homochromatic FM were classified correctly by using the SVM model. To summarized, hyperspectral images combined with pattern recognition are an effective method for recognizing homochromatic FM in mixed congee.